Query Computation Time and Its Performance over Distributed Database Frameworks
D Ravikiran1, S.V. Naga Srinivasu2

1D Ravikiran, Research Scholar, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, Andhra Pradesh, India.
2S. V Naga Srinivasu, Professor, Narasaraopet Engineering College, Narasaraopet, Andhra Pradesh, India.

Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 5153-5160 | Volume-8 Issue-12, October 2019. | Retrieval Number: L27691081219/2019©BEIESP | DOI: 10.35940/ijitee.L2769.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: It is essential to maintain a relevant methodology for data fragmentation to employ resources, and thus, it needs to choose an accurate and efficient fragmentation methodology to improve authority of distributed database system. This leads the challenges on data reliability, stable storage space and costs, Communication costs, and security issues. In Distributed database framework, query computation and data privacy plays a vital role over portioned distributed databases such as vertical, horizontal and hybrid models, Privacy of any information is regarded as the essential issue in nowadays hence we show an approach by that we can use privacy preservation over the two parties which are actually distributing their data horizontally or vertically. In this chapter, I present an approach by which the concept of hierarchal clustering applied over the horizontally partitioned data set. We also explain the desired algorithm like hierarchal clustering, algorithms for finding the minimum closest cluster. Furthermore, it explores the performance of Query Computation over portioned databases with the analysis of Efficiency and Privacy.
Keywords: Distributed Database, Vertical Partition, Horizontal Partition, query computation.
Scope of the Article: Patterns and Frameworks